Prediction and compensation of subsequent deformation in robotbased incremental sheet forming by application of machine learning

应用机器学习预测和补偿基于机器人的增量板材成形中的后续变形

• 批准号: 457407945
• 负责人: Professor Dr.-Ing. Bernd Kuhlenkötter
• 金额: --
• 依托单位: Lehrstuhl für Produktionssysteme
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/457407945?language=en
• 关键词: Prediction; compensation; subsequent; deformation; robotbased

Incremental sheet forming (ISF) is flexible, workpiece-independent process for manufacturing sheet metal parts in small lot sizes. An industrial application has yet rare or not all taken place due to the still low geometric accuracy of the process. The reason for this is mainly the missing possibility for a precise simulation of the forming process, hindering the use of compensation approaches for springback and subsequent deformation. While there are multiple FEM-based simulation approaches, their application is prevented by summing up simulation errors, caused by the incremental nature of the process. During the research project, a data driven approach will be pursued by the usage of machine learning, which, in contrast to FEM-simulations, does not need a detailed modelling of the forming process. A multi-layer artificial neural network (ANN) will be build up, predicting the resulting geometric accuracy of a forming experiment based on common process parameters, part geometry and the course of the tool path.To be able to train the ANN, a process database will be built up. A preferably wide spectrum of process data will be acquired in an experimental series, in which a systematically varied part will be formed and measured with alternated process parameters. By the this way generated wide range of the training data, a generalisation of the ANN is enabled making it applicable to any part.To take the influence of the part geometry on the resulting geometric accuracy into account, the part geometry will be transformed into a format with a fixed number of parameters which is therefore usable for machine learning. This is achieved by the development of several representation approaches whose performances are evaluated with a quality criterion. This assesses the quality of the approximation and correlation of the parameters of the geometry representation and the part geometry.Utilizing the built up process database and the developed geometry representation with the highest performance, a multi-layer ANN will be trained. Meanwhile its prediction performance is validated with a test dataset gathered in reference forming experiments. Afterwards the trained ANN is used to improve the geometric accuracy of a part by modifying the tool path based on the predicted geometric accuracy. To execute this, existing tool path planning approaches need to be extended as the data driven nature of the ANN can lead to rare prediction errors otherwise resulting in false tool paths.

渐进式板材成形 (ISF) 是一种灵活的、独立于工件的工艺，用于制造小批量的钣金零件。由于该工艺的几何精度仍然较低，工业应用还很少或没有全部发生。其原因主要是缺乏对成形过程进行精确模拟的可能性，阻碍了回弹和后续变形补偿方法的使用。虽然有多种基于 FEM 的仿真方法，但由于过程的增量性质而导致仿真误差的累加，从而阻碍了它们的应用。在研究项目期间，将通过使用机器学习来追求数据驱动的方法，与有限元模拟相比，机器学习不需要对成形过程进行详细的建模。将建立一个多层人工神经网络（ANN），根据常见的工艺参数、零件几何形状和刀具路径路线来预测成形实验的几何精度。为了能够训练 ANN，将建立一个工艺数据库。优选地，将在一系列实验中获取广泛的过程数据，其中将形成系统变化的部件并用交替的过程参数进行测量。通过这种方式生成广泛的训练数据，使人工神经网络能够泛化，使其适用于任何零件。为了考虑零件几何形状对最终几何精度的影响，零件几何形状将被转换为具有固定数量参数的格式，因此可用于机器学习。这是通过开发几种表示方法来实现的，这些方法的性能通过质量标准进行评估。这评估了几何表示和零件几何参数的近似和相关性的质量。利用建立的工艺数据库和开发的具有最高性能的几何表示，将训练多层人工神经网络。同时，它的预测性能通过参考形成实验中收集的测试数据集进行了验证。然后，使用经过训练的 ANN 根据预测的几何精度修改刀具路径，从而提高零件的几何精度。为了执行此操作，需要扩展现有的刀具路径规划方法，因为人工神经网络的数据驱动性质可能会导致罕见的预测错误，否则会导致错误的刀具路径。